如果Y^L 中的每個r都有自己的最近鄰居Y(hat)^L_g，則max RS(bar) (v， r)很大，因而ID-MRF損失較小。這里，主要思想是強制/引導生成的特征patch有不同的最近鄰居，從而生成的feature具有更好的局部紋理。
與先前的工作一樣，作者使用預先訓練的VGG19來計算ID-MRF損失。請注意，中間層conv3_2和conv4_2分別表示結構特征和語義特征。
作者聲稱這種損失與最近鄰搜索有關，并且只在訓練階段使用。這與在測試階段搜索最近鄰居的方法不同。
特征不變重建損失
所提出的空間變異重構損失實際上是一個加權的L1損失。確定權值的方法有很多種，本文利用高斯濾波器對掩模進行卷積，生成加權掩模，計算加權L1損耗。加權L1損失的主要思想是，接近有效像素的像素損失比遠離有效像素的像素損失受到更高的約束。因此，位于缺失區域中心的像素損失應該有更低的L1損失權值(即更少的約束)。
對抗損失
與先前的工作類似，作者采用了改進的WGAN損失和局部和全局鑒別器。
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